I’ve been thinking about microservices a lot lately. Specifically, how we can build systems that not only scale but remain understandable when things inevitably go wrong. That’s why I want to share my approach to creating production-ready event-driven microservices using Go, NATS JetStream, and OpenTelemetry. These tools have fundamentally changed how I think about distributed systems.

What makes an event-driven architecture so powerful? It’s the decoupling. Services communicate through events rather than direct calls, which means they can evolve independently. But this freedom comes with challenges: how do we ensure messages aren’t lost? How do we trace a request across service boundaries?

NATS JetStream provides durable message streaming with exactly-once delivery semantics. This isn’t just another message queue—it’s a foundation for building reliable systems. When combined with Go’s excellent concurrency primitives, we get both performance and reliability.

Let me show you a practical example. Here’s how we set up a basic JetStream connection:

nc, err := nats.Connect("nats://localhost:4222",
    nats.ReconnectWait(2*time.Second),
    nats.MaxReconnects(-1),
)
if err != nil {
    return fmt.Errorf("failed to connect: %w", err)
}

js, err := nc.JetStream()
if err != nil {
    return fmt.Errorf("jetstream context failed: %w", err)
}

But connectivity is just the beginning. Have you ever wondered how to ensure messages are processed exactly once, even when services restart? JetStream’s durable consumers and message acknowledgements handle this elegantly.

Observability is where OpenTelemetry transforms our ability to understand distributed systems. Without proper tracing, debugging across service boundaries becomes guesswork. Here’s how we instrument a simple span:

ctx, span := tracer.Start(ctx, "process_order")
defer span.End()

span.SetAttributes(
    attribute.String("order.id", orderID),
    attribute.Int("item.count", len(items)),
)

When we combine NATS with OpenTelemetry, we get distributed tracing that follows messages across services. This isn’t just theoretical—it’s practical magic that saves hours of debugging.

Error handling in event-driven systems requires a different mindset. Instead of immediate failures, we need strategies for retries, dead-letter queues, and circuit breakers. How do you handle a service that’s temporarily unavailable without losing messages?

Here’s a pattern I use for resilient message processing:

func processWithRetry(msg *nats.Msg, maxAttempts int) error {
    for attempt := 1; attempt <= maxAttempts; attempt++ {
        err := processMessage(msg)
        if err == nil {
            return nil
        }
        
        if shouldRetry(err) {
            time.Sleep(backoffDuration(attempt))
            continue
        }
        
        return err
    }
    return fmt.Errorf("max retries exceeded")
}

Testing event-driven systems presents unique challenges. We need to verify not just function outputs but also the events that get published. I’ve found that testing the entire flow—from command to event—yields the most confidence.

Deployment considerations are equally important. How do we ensure our microservices can handle traffic spikes? Docker containers combined with proper resource limits and health checks create a solid foundation. Prometheus metrics give us the visibility we need to scale appropriately.

The beauty of this architecture lies in its flexibility. New services can join the ecosystem simply by subscribing to relevant events. Existing services can be updated without breaking downstream consumers. This evolutionary capability is crucial for long-term maintainability.

What separates a proof-of-concept from a production system? It’s the attention to details: proper logging, metrics, tracing, and fault tolerance. These aren’t nice-to-haves—they’re essential for systems that people depend on.

I encourage you to try building with these patterns. Start small, focus on the fundamentals, and gradually add complexity. The investment in learning these tools pays dividends in system reliability and developer productivity.

If you found this useful, please share it with others who might benefit. I’d love to hear about your experiences with event-driven architectures—what challenges have you faced? What solutions have worked for you? Let’s continue the conversation in the comments.